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Theoria. Revista de Teoría, Historia y Fundamentos de la Ciencia

Volume 26, Issue 1, January 2011

Lorenzo Casini, Phyllis Mckay Illari, Federica Russo, Jon Williamson
Pages 5-33

Models for Prediction, Explanation and Control
Recursive Bayesian Networks

The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show in particular how a simple two-level RBN can be used to model a mechanism in cancer science. The higher level of our model contains variables at the clinical level, while the lower level maps the structure of the cell’s mechanism for apoptosis.

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